Efficient Poisson Subsampling for the Partially Linear Additive Cox Model

Abstract

To address the computational and storage challenges often encountered in large-scale survival data analysis, we propose an efficient Poisson subsampling method for the partially linear additive Cox model. This model provides a flexible yet interpretable framework by incorporating linear covariate effects, additive nonparametric components for nonlinear covariates, and a nonparametric baseline hazard function. The proposed method adopts B-spline basis functions to approximate the nonparametric components and employs the decorrelated score technique to construct a Poisson subsampling-based estimation equation, based on which we establish the asymptotic normality of the resulting estimator and derive the optimal subsampling probabilities according to the L-optimality criterion. Furthermore, we design a two-step adaptive algorithm for practical implementation. The proposed approach enables computationally efficient statistical inference for large-scale survival analysis without processing the full dataset. We validate the performance of the proposed method through extensive simulation studies and a real-world application to a lymphoma cancer dataset, demonstrating its efficiency and accuracy in large-scale settings.